Liquid circular angular accelerometer-based incipient bearing fault diagnosis

This paper explores the application of a liquid circular angular accelerometer (LCAA) in incipient bearing fault diagnosis. First, a wireless instantaneous angular acceleration (IAA) signal acquisition system is designed to collect motor IAA under various bearing fault conditions. Then, the IAA characteristics of the motor with both healthy bearings and incipient bearing faults are analyzed, which provides valuable insights into fault diagnosis method design. The proposed method implements an advanced signal preprocessing technique, which is developed based on self-adaptive noise cancellation (separates discrete frequency noises), minimum entropy deconvolution (enhances the fault-related components), and a novel approach of sliding time-window analysis to improve reliability. Hereafter, IAA-based estimated fault characteristic frequencies are identified in the envelope spectra of the post-processed data, which finalizes the bearing fault diagnosis. Simulation and experimental results substantiate the effectiveness of the proposed approach for early fault detection, even under the conditions of low sampling rates.